de Castro, Leandro N. and Timmis, Jon
(2002)
Hierarchy and Convergance of Immune Networks: Basic Ideas and Premilinary Results.
In: 1st International Conference on Artificial Immune Systems, September 9th-11th 2002, University of Kent at Canterbury, UK.
(The full text of this publication is not available from this repository)

Abstract

aiNet is an artificial immune network model originally developed to perform automatic data compression. Combined with graph theoretical and statistical clustering techniques, aiNet is a powerful data clustering and classification tool. However, the original aiNet model suffers from the lack of a well-defined stopping criterion and an ad hoc approach to parameter initialization, prior to the training process. This paper has two main goals. First, by assessing convergence criteria employed in a class of artificial neural networks, a suitable stopping criterion can be created for aiNet. Secondly, the paper demonstrates that through the use of a cooling schedule for some of these user-defined parameters, it is not only possible to reduce the importance of their initial values, but also this leads to possible derivation of a hierarchical tree of immune networks. Due to the very limited space available, only the basic ideas of a novel convergence criterion, and an approach to develop a tree of aiNets will be presented, together with an illustrative example.